Building a Machine Learning Model: The Complete Workflow
Machine Learning is an art as much as it is a science. Here's a visual guide to the journey from raw data to a fully trained and optimized model.
1. Initial Dataset: It all begins with collecting input and output variables.
2. Exploratory Data Analysis (EDA): Understanding your data is crucial. Techniques like PCA and SOM help in extracting the essence of your dataset.
3. Data Preprocessing: Data cleaning, curation, and removal of redundant features ensure the dataset is ready for modeling.
4. Data Splitting: The data is split into a training set (80%) and a test set (20%).
5. Model Training: Various algorithms (SVM, DL, KNN, GBM, RF, DT) are tested with feature selection and hyperparameter optimization.
6. Cross-Validation: Ensuring the model is robust and performs well across different subsets of data.
7. Model Evaluation: Finally, classification models are evaluated using metrics like MCC, Specificity, Sensitivity, and Accuracy. For regression models, RMSE, R², and MSE are key indicators of performance.
8. Predicted Outcomes: The result is a trained model ready to make predictions!
A meticulous approach leads to a model that not only performs well on the test data but is also generalizable to new, unseen data.
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